Prosper Loans Exploration by Paron Sarampakhul
## [1] 113937 81## 'data.frame': 113937 obs. of 81 variables:
## $ ListingKey : Factor w/ 113066 levels "00003546482094282EF90E5",..: 7180 7193 6647 6669 6686 6689 6699 6706 6687 6687 ...
## $ ListingNumber : int 193129 1209647 81716 658116 909464 1074836 750899 768193 1023355 1023355 ...
## $ ListingCreationDate : Factor w/ 113064 levels "2005-11-09 20:44:28.847000000",..: 14184 111894 6429 64760 85967 100310 72556 74019 97834 97834 ...
## $ CreditGrade : Factor w/ 9 levels "","A","AA","B",..: 5 1 8 1 1 1 1 1 1 1 ...
## $ Term : int 36 36 36 36 36 60 36 36 36 36 ...
## $ LoanStatus : Factor w/ 12 levels "Cancelled","Chargedoff",..: 3 4 3 4 4 4 4 4 4 4 ...
## $ ClosedDate : Factor w/ 2803 levels "","2005-11-25 00:00:00",..: 1138 1 1263 1 1 1 1 1 1 1 ...
## $ BorrowerAPR : num 0.165 0.12 0.283 0.125 0.246 ...
## $ BorrowerRate : num 0.158 0.092 0.275 0.0974 0.2085 ...
## $ LenderYield : num 0.138 0.082 0.24 0.0874 0.1985 ...
## $ EstimatedEffectiveYield : num NA 0.0796 NA 0.0849 0.1832 ...
## $ EstimatedLoss : num NA 0.0249 NA 0.0249 0.0925 ...
## $ EstimatedReturn : num NA 0.0547 NA 0.06 0.0907 ...
## $ ProsperRating..numeric. : int NA 6 NA 6 3 5 2 4 7 7 ...
## $ ProsperRating..Alpha. : Factor w/ 8 levels "","A","AA","B",..: 1 2 1 2 6 4 7 5 3 3 ...
## $ ProsperScore : num NA 7 NA 9 4 10 2 4 9 11 ...
## $ ListingCategory..numeric. : int 0 2 0 16 2 1 1 2 7 7 ...
## $ BorrowerState : Factor w/ 52 levels "","AK","AL","AR",..: 7 7 12 12 25 34 18 6 16 16 ...
## $ Occupation : Factor w/ 68 levels "","Accountant/CPA",..: 37 43 37 52 21 43 50 29 24 24 ...
## $ EmploymentStatus : Factor w/ 9 levels "","Employed",..: 9 2 4 2 2 2 2 2 2 2 ...
## $ EmploymentStatusDuration : int 2 44 NA 113 44 82 172 103 269 269 ...
## $ IsBorrowerHomeowner : Factor w/ 2 levels "False","True": 2 1 1 2 2 2 1 1 2 2 ...
## $ CurrentlyInGroup : Factor w/ 2 levels "False","True": 2 1 2 1 1 1 1 1 1 1 ...
## $ GroupKey : Factor w/ 707 levels "","00343376901312423168731",..: 1 1 335 1 1 1 1 1 1 1 ...
## $ DateCreditPulled : Factor w/ 112992 levels "2005-11-09 00:30:04.487000000",..: 14347 111883 6446 64724 85857 100382 72500 73937 97888 97888 ...
## $ CreditScoreRangeLower : int 640 680 480 800 680 740 680 700 820 820 ...
## $ CreditScoreRangeUpper : int 659 699 499 819 699 759 699 719 839 839 ...
## $ FirstRecordedCreditLine : Factor w/ 11586 levels "","1947-08-24 00:00:00",..: 8639 6617 8927 2247 9498 497 8265 7685 5543 5543 ...
## $ CurrentCreditLines : int 5 14 NA 5 19 21 10 6 17 17 ...
## $ OpenCreditLines : int 4 14 NA 5 19 17 7 6 16 16 ...
## $ TotalCreditLinespast7years : int 12 29 3 29 49 49 20 10 32 32 ...
## $ OpenRevolvingAccounts : int 1 13 0 7 6 13 6 5 12 12 ...
## $ OpenRevolvingMonthlyPayment : num 24 389 0 115 220 1410 214 101 219 219 ...
## $ InquiriesLast6Months : int 3 3 0 0 1 0 0 3 1 1 ...
## $ TotalInquiries : num 3 5 1 1 9 2 0 16 6 6 ...
## $ CurrentDelinquencies : int 2 0 1 4 0 0 0 0 0 0 ...
## $ AmountDelinquent : num 472 0 NA 10056 0 ...
## $ DelinquenciesLast7Years : int 4 0 0 14 0 0 0 0 0 0 ...
## $ PublicRecordsLast10Years : int 0 1 0 0 0 0 0 1 0 0 ...
## $ PublicRecordsLast12Months : int 0 0 NA 0 0 0 0 0 0 0 ...
## $ RevolvingCreditBalance : num 0 3989 NA 1444 6193 ...
## $ BankcardUtilization : num 0 0.21 NA 0.04 0.81 0.39 0.72 0.13 0.11 0.11 ...
## $ AvailableBankcardCredit : num 1500 10266 NA 30754 695 ...
## $ TotalTrades : num 11 29 NA 26 39 47 16 10 29 29 ...
## $ TradesNeverDelinquent..percentage. : num 0.81 1 NA 0.76 0.95 1 0.68 0.8 1 1 ...
## $ TradesOpenedLast6Months : num 0 2 NA 0 2 0 0 0 1 1 ...
## $ DebtToIncomeRatio : num 0.17 0.18 0.06 0.15 0.26 0.36 0.27 0.24 0.25 0.25 ...
## $ IncomeRange : Factor w/ 8 levels "$0","$1-24,999",..: 4 5 7 4 3 3 4 4 4 4 ...
## $ IncomeVerifiable : Factor w/ 2 levels "False","True": 2 2 2 2 2 2 2 2 2 2 ...
## $ StatedMonthlyIncome : num 3083 6125 2083 2875 9583 ...
## $ LoanKey : Factor w/ 113066 levels "00003683605746079487FF7",..: 100337 69837 46303 70776 71387 86505 91250 5425 908 908 ...
## $ TotalProsperLoans : int NA NA NA NA 1 NA NA NA NA NA ...
## $ TotalProsperPaymentsBilled : int NA NA NA NA 11 NA NA NA NA NA ...
## $ OnTimeProsperPayments : int NA NA NA NA 11 NA NA NA NA NA ...
## $ ProsperPaymentsLessThanOneMonthLate: int NA NA NA NA 0 NA NA NA NA NA ...
## $ ProsperPaymentsOneMonthPlusLate : int NA NA NA NA 0 NA NA NA NA NA ...
## $ ProsperPrincipalBorrowed : num NA NA NA NA 11000 NA NA NA NA NA ...
## $ ProsperPrincipalOutstanding : num NA NA NA NA 9948 ...
## $ ScorexChangeAtTimeOfListing : int NA NA NA NA NA NA NA NA NA NA ...
## $ LoanCurrentDaysDelinquent : int 0 0 0 0 0 0 0 0 0 0 ...
## $ LoanFirstDefaultedCycleNumber : int NA NA NA NA NA NA NA NA NA NA ...
## $ LoanMonthsSinceOrigination : int 78 0 86 16 6 3 11 10 3 3 ...
## $ LoanNumber : int 19141 134815 6466 77296 102670 123257 88353 90051 121268 121268 ...
## $ LoanOriginalAmount : int 9425 10000 3001 10000 15000 15000 3000 10000 10000 10000 ...
## $ LoanOriginationDate : Factor w/ 1873 levels "2005-11-15 00:00:00",..: 426 1866 260 1535 1757 1821 1649 1666 1813 1813 ...
## $ LoanOriginationQuarter : Factor w/ 33 levels "Q1 2006","Q1 2007",..: 18 8 2 32 24 33 16 16 33 33 ...
## $ MemberKey : Factor w/ 90831 levels "00003397697413387CAF966",..: 11071 10302 33781 54939 19465 48037 60448 40951 26129 26129 ...
## $ MonthlyLoanPayment : num 330 319 123 321 564 ...
## $ LP_CustomerPayments : num 11396 0 4187 5143 2820 ...
## $ LP_CustomerPrincipalPayments : num 9425 0 3001 4091 1563 ...
## $ LP_InterestandFees : num 1971 0 1186 1052 1257 ...
## $ LP_ServiceFees : num -133.2 0 -24.2 -108 -60.3 ...
## $ LP_CollectionFees : num 0 0 0 0 0 0 0 0 0 0 ...
## $ LP_GrossPrincipalLoss : num 0 0 0 0 0 0 0 0 0 0 ...
## $ LP_NetPrincipalLoss : num 0 0 0 0 0 0 0 0 0 0 ...
## $ LP_NonPrincipalRecoverypayments : num 0 0 0 0 0 0 0 0 0 0 ...
## $ PercentFunded : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Recommendations : int 0 0 0 0 0 0 0 0 0 0 ...
## $ InvestmentFromFriendsCount : int 0 0 0 0 0 0 0 0 0 0 ...
## $ InvestmentFromFriendsAmount : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Investors : int 258 1 41 158 20 1 1 1 1 1 ...## ListingKey ListingNumber
## 17A93590655669644DB4C06: 6 Min. : 4
## 349D3587495831350F0F648: 4 1st Qu.: 400919
## 47C1359638497431975670B: 4 Median : 600554
## 8474358854651984137201C: 4 Mean : 627886
## DE8535960513435199406CE: 4 3rd Qu.: 892634
## 04C13599434217079754AEE: 3 Max. :1255725
## (Other) :113912
## ListingCreationDate CreditGrade Term
## 2013-10-02 17:20:16.550000000: 6 :84984 Min. :12.00
## 2013-08-28 20:31:41.107000000: 4 C : 5649 1st Qu.:36.00
## 2013-09-08 09:27:44.853000000: 4 D : 5153 Median :36.00
## 2013-12-06 05:43:13.830000000: 4 B : 4389 Mean :40.83
## 2013-12-06 11:44:58.283000000: 4 AA : 3509 3rd Qu.:36.00
## 2013-08-21 07:25:22.360000000: 3 HR : 3508 Max. :60.00
## (Other) :113912 (Other): 6745
## LoanStatus ClosedDate
## Current :56576 :58848
## Completed :38074 2014-03-04 00:00:00: 105
## Chargedoff :11992 2014-02-19 00:00:00: 100
## Defaulted : 5018 2014-02-11 00:00:00: 92
## Past Due (1-15 days) : 806 2012-10-30 00:00:00: 81
## Past Due (31-60 days): 363 2013-02-26 00:00:00: 78
## (Other) : 1108 (Other) :54633
## BorrowerAPR BorrowerRate LenderYield
## Min. :0.00653 Min. :0.0000 Min. :-0.0100
## 1st Qu.:0.15629 1st Qu.:0.1340 1st Qu.: 0.1242
## Median :0.20976 Median :0.1840 Median : 0.1730
## Mean :0.21883 Mean :0.1928 Mean : 0.1827
## 3rd Qu.:0.28381 3rd Qu.:0.2500 3rd Qu.: 0.2400
## Max. :0.51229 Max. :0.4975 Max. : 0.4925
## NA's :25
## EstimatedEffectiveYield EstimatedLoss EstimatedReturn
## Min. :-0.183 Min. :0.005 Min. :-0.183
## 1st Qu.: 0.116 1st Qu.:0.042 1st Qu.: 0.074
## Median : 0.162 Median :0.072 Median : 0.092
## Mean : 0.169 Mean :0.080 Mean : 0.096
## 3rd Qu.: 0.224 3rd Qu.:0.112 3rd Qu.: 0.117
## Max. : 0.320 Max. :0.366 Max. : 0.284
## NA's :29084 NA's :29084 NA's :29084
## ProsperRating..numeric. ProsperRating..Alpha. ProsperScore
## Min. :1.000 :29084 Min. : 1.00
## 1st Qu.:3.000 C :18345 1st Qu.: 4.00
## Median :4.000 B :15581 Median : 6.00
## Mean :4.072 A :14551 Mean : 5.95
## 3rd Qu.:5.000 D :14274 3rd Qu.: 8.00
## Max. :7.000 E : 9795 Max. :11.00
## NA's :29084 (Other):12307 NA's :29084
## ListingCategory..numeric. BorrowerState
## Min. : 0.000 CA :14717
## 1st Qu.: 1.000 TX : 6842
## Median : 1.000 NY : 6729
## Mean : 2.774 FL : 6720
## 3rd Qu.: 3.000 IL : 5921
## Max. :20.000 : 5515
## (Other):67493
## Occupation EmploymentStatus
## Other :28617 Employed :67322
## Professional :13628 Full-time :26355
## Computer Programmer : 4478 Self-employed: 6134
## Executive : 4311 Not available: 5347
## Teacher : 3759 Other : 3806
## Administrative Assistant: 3688 : 2255
## (Other) :55456 (Other) : 2718
## EmploymentStatusDuration IsBorrowerHomeowner CurrentlyInGroup
## Min. : 0.00 False:56459 False:101218
## 1st Qu.: 26.00 True :57478 True : 12719
## Median : 67.00
## Mean : 96.07
## 3rd Qu.:137.00
## Max. :755.00
## NA's :7625
## GroupKey DateCreditPulled
## :100596 2013-12-23 09:38:12: 6
## 783C3371218786870A73D20: 1140 2013-11-21 09:09:41: 4
## 3D4D3366260257624AB272D: 916 2013-12-06 05:43:16: 4
## 6A3B336601725506917317E: 698 2014-01-14 20:17:49: 4
## FEF83377364176536637E50: 611 2014-02-09 12:14:41: 4
## C9643379247860156A00EC0: 342 2013-09-27 22:04:54: 3
## (Other) : 9634 (Other) :113912
## CreditScoreRangeLower CreditScoreRangeUpper
## Min. : 0.0 Min. : 19.0
## 1st Qu.:660.0 1st Qu.:679.0
## Median :680.0 Median :699.0
## Mean :685.6 Mean :704.6
## 3rd Qu.:720.0 3rd Qu.:739.0
## Max. :880.0 Max. :899.0
## NA's :591 NA's :591
## FirstRecordedCreditLine CurrentCreditLines OpenCreditLines
## : 697 Min. : 0.00 Min. : 0.00
## 1993-12-01 00:00:00: 185 1st Qu.: 7.00 1st Qu.: 6.00
## 1994-11-01 00:00:00: 178 Median :10.00 Median : 9.00
## 1995-11-01 00:00:00: 168 Mean :10.32 Mean : 9.26
## 1990-04-01 00:00:00: 161 3rd Qu.:13.00 3rd Qu.:12.00
## 1995-03-01 00:00:00: 159 Max. :59.00 Max. :54.00
## (Other) :112389 NA's :7604 NA's :7604
## TotalCreditLinespast7years OpenRevolvingAccounts
## Min. : 2.00 Min. : 0.00
## 1st Qu.: 17.00 1st Qu.: 4.00
## Median : 25.00 Median : 6.00
## Mean : 26.75 Mean : 6.97
## 3rd Qu.: 35.00 3rd Qu.: 9.00
## Max. :136.00 Max. :51.00
## NA's :697
## OpenRevolvingMonthlyPayment InquiriesLast6Months TotalInquiries
## Min. : 0.0 Min. : 0.000 Min. : 0.000
## 1st Qu.: 114.0 1st Qu.: 0.000 1st Qu.: 2.000
## Median : 271.0 Median : 1.000 Median : 4.000
## Mean : 398.3 Mean : 1.435 Mean : 5.584
## 3rd Qu.: 525.0 3rd Qu.: 2.000 3rd Qu.: 7.000
## Max. :14985.0 Max. :105.000 Max. :379.000
## NA's :697 NA's :1159
## CurrentDelinquencies AmountDelinquent DelinquenciesLast7Years
## Min. : 0.0000 Min. : 0.0 Min. : 0.000
## 1st Qu.: 0.0000 1st Qu.: 0.0 1st Qu.: 0.000
## Median : 0.0000 Median : 0.0 Median : 0.000
## Mean : 0.5921 Mean : 984.5 Mean : 4.155
## 3rd Qu.: 0.0000 3rd Qu.: 0.0 3rd Qu.: 3.000
## Max. :83.0000 Max. :463881.0 Max. :99.000
## NA's :697 NA's :7622 NA's :990
## PublicRecordsLast10Years PublicRecordsLast12Months RevolvingCreditBalance
## Min. : 0.0000 Min. : 0.000 Min. : 0
## 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 3121
## Median : 0.0000 Median : 0.000 Median : 8549
## Mean : 0.3126 Mean : 0.015 Mean : 17599
## 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 19521
## Max. :38.0000 Max. :20.000 Max. :1435667
## NA's :697 NA's :7604 NA's :7604
## BankcardUtilization AvailableBankcardCredit TotalTrades
## Min. :0.000 Min. : 0 Min. : 0.00
## 1st Qu.:0.310 1st Qu.: 880 1st Qu.: 15.00
## Median :0.600 Median : 4100 Median : 22.00
## Mean :0.561 Mean : 11210 Mean : 23.23
## 3rd Qu.:0.840 3rd Qu.: 13180 3rd Qu.: 30.00
## Max. :5.950 Max. :646285 Max. :126.00
## NA's :7604 NA's :7544 NA's :7544
## TradesNeverDelinquent..percentage. TradesOpenedLast6Months
## Min. :0.000 Min. : 0.000
## 1st Qu.:0.820 1st Qu.: 0.000
## Median :0.940 Median : 0.000
## Mean :0.886 Mean : 0.802
## 3rd Qu.:1.000 3rd Qu.: 1.000
## Max. :1.000 Max. :20.000
## NA's :7544 NA's :7544
## DebtToIncomeRatio IncomeRange IncomeVerifiable
## Min. : 0.000 $25,000-49,999:32192 False: 8669
## 1st Qu.: 0.140 $50,000-74,999:31050 True :105268
## Median : 0.220 $100,000+ :17337
## Mean : 0.276 $75,000-99,999:16916
## 3rd Qu.: 0.320 Not displayed : 7741
## Max. :10.010 $1-24,999 : 7274
## NA's :8554 (Other) : 1427
## StatedMonthlyIncome LoanKey TotalProsperLoans
## Min. : 0 CB1B37030986463208432A1: 6 Min. :0.00
## 1st Qu.: 3200 2DEE3698211017519D7333F: 4 1st Qu.:1.00
## Median : 4667 9F4B37043517554537C364C: 4 Median :1.00
## Mean : 5608 D895370150591392337ED6D: 4 Mean :1.42
## 3rd Qu.: 6825 E6FB37073953690388BC56D: 4 3rd Qu.:2.00
## Max. :1750003 0D8F37036734373301ED419: 3 Max. :8.00
## (Other) :113912 NA's :91852
## TotalProsperPaymentsBilled OnTimeProsperPayments
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 9.00 1st Qu.: 9.00
## Median : 16.00 Median : 15.00
## Mean : 22.93 Mean : 22.27
## 3rd Qu.: 33.00 3rd Qu.: 32.00
## Max. :141.00 Max. :141.00
## NA's :91852 NA's :91852
## ProsperPaymentsLessThanOneMonthLate ProsperPaymentsOneMonthPlusLate
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 0.00 Median : 0.00
## Mean : 0.61 Mean : 0.05
## 3rd Qu.: 0.00 3rd Qu.: 0.00
## Max. :42.00 Max. :21.00
## NA's :91852 NA's :91852
## ProsperPrincipalBorrowed ProsperPrincipalOutstanding
## Min. : 0 Min. : 0
## 1st Qu.: 3500 1st Qu.: 0
## Median : 6000 Median : 1627
## Mean : 8472 Mean : 2930
## 3rd Qu.:11000 3rd Qu.: 4127
## Max. :72499 Max. :23451
## NA's :91852 NA's :91852
## ScorexChangeAtTimeOfListing LoanCurrentDaysDelinquent
## Min. :-209.00 Min. : 0.0
## 1st Qu.: -35.00 1st Qu.: 0.0
## Median : -3.00 Median : 0.0
## Mean : -3.22 Mean : 152.8
## 3rd Qu.: 25.00 3rd Qu.: 0.0
## Max. : 286.00 Max. :2704.0
## NA's :95009
## LoanFirstDefaultedCycleNumber LoanMonthsSinceOrigination LoanNumber
## Min. : 0.00 Min. : 0.0 Min. : 1
## 1st Qu.: 9.00 1st Qu.: 6.0 1st Qu.: 37332
## Median :14.00 Median : 21.0 Median : 68599
## Mean :16.27 Mean : 31.9 Mean : 69444
## 3rd Qu.:22.00 3rd Qu.: 65.0 3rd Qu.:101901
## Max. :44.00 Max. :100.0 Max. :136486
## NA's :96985
## LoanOriginalAmount LoanOriginationDate LoanOriginationQuarter
## Min. : 1000 2014-01-22 00:00:00: 491 Q4 2013:14450
## 1st Qu.: 4000 2013-11-13 00:00:00: 490 Q1 2014:12172
## Median : 6500 2014-02-19 00:00:00: 439 Q3 2013: 9180
## Mean : 8337 2013-10-16 00:00:00: 434 Q2 2013: 7099
## 3rd Qu.:12000 2014-01-28 00:00:00: 339 Q3 2012: 5632
## Max. :35000 2013-09-24 00:00:00: 316 Q2 2012: 5061
## (Other) :111428 (Other):60343
## MemberKey MonthlyLoanPayment LP_CustomerPayments
## 63CA34120866140639431C9: 9 Min. : 0.0 Min. : -2.35
## 16083364744933457E57FB9: 8 1st Qu.: 131.6 1st Qu.: 1005.76
## 3A2F3380477699707C81385: 8 Median : 217.7 Median : 2583.83
## 4D9C3403302047712AD0CDD: 8 Mean : 272.5 Mean : 4183.08
## 739C338135235294782AE75: 8 3rd Qu.: 371.6 3rd Qu.: 5548.40
## 7E1733653050264822FAA3D: 8 Max. :2251.5 Max. :40702.39
## (Other) :113888
## LP_CustomerPrincipalPayments LP_InterestandFees LP_ServiceFees
## Min. : 0.0 Min. : -2.35 Min. :-664.87
## 1st Qu.: 500.9 1st Qu.: 274.87 1st Qu.: -73.18
## Median : 1587.5 Median : 700.84 Median : -34.44
## Mean : 3105.5 Mean : 1077.54 Mean : -54.73
## 3rd Qu.: 4000.0 3rd Qu.: 1458.54 3rd Qu.: -13.92
## Max. :35000.0 Max. :15617.03 Max. : 32.06
##
## LP_CollectionFees LP_GrossPrincipalLoss LP_NetPrincipalLoss
## Min. :-9274.75 Min. : -94.2 Min. : -954.5
## 1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.: 0.0
## Median : 0.00 Median : 0.0 Median : 0.0
## Mean : -14.24 Mean : 700.4 Mean : 681.4
## 3rd Qu.: 0.00 3rd Qu.: 0.0 3rd Qu.: 0.0
## Max. : 0.00 Max. :25000.0 Max. :25000.0
##
## LP_NonPrincipalRecoverypayments PercentFunded Recommendations
## Min. : 0.00 Min. :0.7000 Min. : 0.00000
## 1st Qu.: 0.00 1st Qu.:1.0000 1st Qu.: 0.00000
## Median : 0.00 Median :1.0000 Median : 0.00000
## Mean : 25.14 Mean :0.9986 Mean : 0.04803
## 3rd Qu.: 0.00 3rd Qu.:1.0000 3rd Qu.: 0.00000
## Max. :21117.90 Max. :1.0125 Max. :39.00000
##
## InvestmentFromFriendsCount InvestmentFromFriendsAmount Investors
## Min. : 0.00000 Min. : 0.00 Min. : 1.00
## 1st Qu.: 0.00000 1st Qu.: 0.00 1st Qu.: 2.00
## Median : 0.00000 Median : 0.00 Median : 44.00
## Mean : 0.02346 Mean : 16.55 Mean : 80.48
## 3rd Qu.: 0.00000 3rd Qu.: 0.00 3rd Qu.: 115.00
## Max. :33.00000 Max. :25000.00 Max. :1189.00
## The dataset contains 81 variables with 113,937 observations.
Univariate Plots Section
I see that from the dataset that there are 2 columns for credit grades,
pre-2009 and after July 2009, therefore I want to see the number of listings
over time. It turns out that there is a gap between late 2008 and mid 2009
where there are no listings. I wonder whether the gap has something to do
with the Financial Crisis in late 2008. There is an increasing trend for
number of listings from 2009 to 2013. This could be because the economy
started to recover.
I want to see if the distributions of credit ratings are the same for
pre-2009 listings and post-2009 listings. I have to re-arrange the ratings from
best to worse for the x-axis. Pre-2009 listings have high numbers of B-rated,
C-rated, and D-rated ratings. The number of AA-rated listings is also more than
the number of A-rated listings. Post-2009 listings have high numbers of
A-rated, B-rated, C-rated, and D-rated listings. The number of AA-rated
listings is significantly lower than the number of A-rated listings.
I want to see the status of the loans. I have to re-arrange the status of
the loans that are past due.
I want to see the distribution of ProsperScore, which is a custom risk score
built using historical Prosper data. From the provided data variables
definitions, the score should be from 1 to 10, with 10 being the best score.
I wonder why there are some listings with score equal to 11.
##
## 12 36 60
## 1614 87778 24545There are only 3 available terms for loans: 12 months, 36 months and 60
months. Most listings are due in 36 months.
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00653 0.15629 0.20976 0.21883 0.28381 0.51229 25## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.1340 0.1840 0.1928 0.2500 0.4975Both BorrowerAPR and Borrowerate have normal distribution. Mean Borrower APR is 21.88% and mean borrower rate is 19.28%. This makes sense because APR is a broader measure of the cost of a mortgage. It includes the interest rate (borrower rate) plus other costs such as broker fees, discount points and some closing costs (https://www.bankrate.com/finance/mortgages/apr-and-interest-rate.aspx).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.0100 0.1242 0.1730 0.1827 0.2400 0.4925## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -0.183 0.116 0.162 0.169 0.224 0.320 29084## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.005 0.042 0.072 0.080 0.112 0.366 29084## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -0.183 0.074 0.092 0.096 0.117 0.284 29084Mean lender yield (interest rate less servicing fee) = 18.27%, mean
estimated effective yield = 16.9%, mean estimated loss = 8%, mean estimated
return = 9.6%. Lender yield, estimated effective yield and estimated loss
have normal distribution but with a small peak on the right side. It seems
that the higher returns are associated with higher risk of loss. The net
estimated return therefore has normal distribution without a small peak
on the right side.
Majority of the loan category is debt consolidation.
The histogram for occupation shows 2 significant peaks: Other and
Professional. This is because the term ‘Professional’ is a broard term,
and ‘Other’ is a blanket term for other occupations that may not be listed
in the survey.
The histogram for employment status shows that most borrowers are employed.
I wonder what are the differnece between ‘Employed’ and ‘Full-time’.
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 26.00 67.00 96.07 137.00 755.00 7625Histogram for employment duration shows that majority of the borrowers have
been employed less than 6 years. The median duration is 67 months.
The portion of borrowers who are homeowners and the portion of borrowers who are not homeowners are close.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 38404 56000 67296 81900 21000035The income range of borrowers are left-skewd normally distributed, with
mean stated annual income = $67,296 and median stated annual income = $56,000.
Since the lower range and upper range of credit score provided by a
consumer credit rating agency, I take the mid points and plot them. The
credit score of the borrowers are left-skewed normally distributed. I suspect
that one of the factors used to determine a credit score is the income.
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 7.00 10.00 10.32 13.00 59.00 7604## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 6.00 9.00 9.26 12.00 54.00 7604## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 4.00 6.00 6.97 9.00 51.00Number of current credit lines, number of open credit lines, and number of
open revolving accounts are right-skewed normally distributed. I wonder whether
these are correlated with the borrower’s credit score, the listing’s credit
grade, and listing’s estimated loss.
Univariate Analysis
What is the structure of your dataset?
The data set consists of 81 variables and 113,937 records. Each record contains the information of the loan listing, which include loan key, interest rate, estimated return rate, estimated loss rate, loan amount, loan category, credit rating, borrower information, etc.
What is/are the main feature(s) of interest in your dataset?
The main features in the dataset are borrower rate, estimated effective yield, estimated loss rate, estimated return rate, and credit rating of a loan. I would like to find out what features determine the mentioned values. I suspect that the main factors should include the annual income of borrower, employment status, credit score, number of open credit lines, number of open revolving accounts, current delinquencies, and amount delinquent.
I suspect that the high annual income and employed status will result in higher credit score. The small number of credit lines, revolving accounts, and delinquencies will also result in higher credit score. Higher credit score should result in lower borrower rate, lower estimated loss, and better credit rating of the listing.
What other features in the dataset do you think will help support your nvestigation into your feature(s) of interest?
Other factors that may determine the credit score include the size of the loan orignal amount, employment duration, TotalProsperPaymentsBilled, %OnTimeProsperPayments vs %LateProsperPayments, ProsperPrincipalOutstanding, and number of recommendations the borrower receives.
Did you create any new variables from existing variables in the dataset?
I only transformed the monthly stated income to annual by multiplying with 12 as I would like to see whether the monthly stated income tallies with the column IncomeRange in terms of their distributions.
I also find the mid point of borrower’s credit score by summing lower range and upper range, and then dividing by 2.
Of the features you investigated, were there any unusual distributions?
I log-transformed the right skewed of employment durations, revolving monthly payments, amount delinquent, and the size of orignal loan amount.
Bivariate Plots Section
## BorrowerRate EstimatedLoss EstimatedReturn
## BorrowerRate 1.000000000 0.90718659 0.744026264
## EstimatedLoss 0.907186588 1.00000000 0.398417591
## EstimatedReturn 0.744026264 0.39841759 1.000000000
## ProsperRating..numeric. -0.952319844 -0.94049755 -0.585215344
## ProsperScore -0.754924462 -0.71339475 -0.511464942
## EmploymentStatusDuration -0.006677846 -0.01104844 -0.001880237
## CreditScoreRangeLower -0.677637574 -0.63777703 -0.460929838
## CreditScoreRangeUpper -0.677637574 -0.63777703 -0.460929838
## OpenCreditLines -0.014502203 -0.02081602 -0.010145916
## OpenRevolvingAccounts -0.053994561 -0.06154182 -0.027623643
## OpenRevolvingMonthlyPayment -0.011158846 -0.02988925 0.016209249
## CurrentDelinquencies 0.192472078 0.20761201 0.091677962
## AmountDelinquent 0.058324323 0.05694464 0.035214686
## StatedMonthlyIncome -0.175402514 -0.16310931 -0.129163303
## TotalProsperPaymentsBilled 0.015204584 0.03754666 -0.026208202
## OnTimeProsperPayments 0.002647098 0.02742060 -0.037520478
## LoanOriginalAmount -0.332415965 -0.37341817 -0.140509818
## Recommendations -0.007944508 0.01546091 -0.033922843
## ProsperRating..numeric. ProsperScore
## BorrowerRate -0.952319844 -0.754924462
## EstimatedLoss -0.940497550 -0.713394750
## EstimatedReturn -0.585215344 -0.511464942
## ProsperRating..numeric. 1.000000000 0.757705549
## ProsperScore 0.757705549 1.000000000
## EmploymentStatusDuration 0.009967765 -0.021738498
## CreditScoreRangeLower 0.693808904 0.468153263
## CreditScoreRangeUpper 0.693808904 0.468153263
## OpenCreditLines 0.019472625 -0.003469519
## OpenRevolvingAccounts 0.059926376 0.051447785
## OpenRevolvingMonthlyPayment 0.018444291 0.004127153
## CurrentDelinquencies -0.191535615 -0.180545589
## AmountDelinquent -0.058112148 -0.069222221
## StatedMonthlyIncome 0.182653529 0.149059321
## TotalProsperPaymentsBilled -0.028280944 0.048789718
## OnTimeProsperPayments -0.016693974 0.060967879
## LoanOriginalAmount 0.378456072 0.265941623
## Recommendations -0.001580269 0.028654751
## EmploymentStatusDuration CreditScoreRangeLower
## BorrowerRate -0.006677846 -0.67763757
## EstimatedLoss -0.011048442 -0.63777703
## EstimatedReturn -0.001880237 -0.46092984
## ProsperRating..numeric. 0.009967765 0.69380890
## ProsperScore -0.021738498 0.46815326
## EmploymentStatusDuration 1.000000000 0.02803261
## CreditScoreRangeLower 0.028032614 1.00000000
## CreditScoreRangeUpper 0.028032614 1.00000000
## OpenCreditLines 0.101591066 0.04730214
## OpenRevolvingAccounts 0.109458477 0.06219749
## OpenRevolvingMonthlyPayment 0.130917140 0.01548968
## CurrentDelinquencies 0.040869571 -0.20293154
## AmountDelinquent 0.018650875 -0.05950110
## StatedMonthlyIncome 0.088275588 0.14136536
## TotalProsperPaymentsBilled 0.052261809 -0.06595384
## OnTimeProsperPayments 0.053694338 -0.05340699
## LoanOriginalAmount 0.021701617 0.29649322
## Recommendations -0.010011644 0.01240691
## CreditScoreRangeUpper OpenCreditLines
## BorrowerRate -0.67763757 -0.014502203
## EstimatedLoss -0.63777703 -0.020816018
## EstimatedReturn -0.46092984 -0.010145916
## ProsperRating..numeric. 0.69380890 0.019472625
## ProsperScore 0.46815326 -0.003469519
## EmploymentStatusDuration 0.02803261 0.101591066
## CreditScoreRangeLower 1.00000000 0.047302142
## CreditScoreRangeUpper 1.00000000 0.047302142
## OpenCreditLines 0.04730214 1.000000000
## OpenRevolvingAccounts 0.06219749 0.874976944
## OpenRevolvingMonthlyPayment 0.01548968 0.549298914
## CurrentDelinquencies -0.20293154 -0.133749451
## AmountDelinquent -0.05950110 -0.071660530
## StatedMonthlyIncome 0.14136536 0.245719475
## TotalProsperPaymentsBilled -0.06595384 0.054588933
## OnTimeProsperPayments -0.05340699 0.062047323
## LoanOriginalAmount 0.29649322 0.138424850
## Recommendations 0.01240691 0.003112712
## OpenRevolvingAccounts
## BorrowerRate -0.05399456
## EstimatedLoss -0.06154182
## EstimatedReturn -0.02762364
## ProsperRating..numeric. 0.05992638
## ProsperScore 0.05144779
## EmploymentStatusDuration 0.10945848
## CreditScoreRangeLower 0.06219749
## CreditScoreRangeUpper 0.06219749
## OpenCreditLines 0.87497694
## OpenRevolvingAccounts 1.00000000
## OpenRevolvingMonthlyPayment 0.56062965
## CurrentDelinquencies -0.13089589
## AmountDelinquent -0.06508789
## StatedMonthlyIncome 0.16756101
## TotalProsperPaymentsBilled 0.01734922
## OnTimeProsperPayments 0.02591960
## LoanOriginalAmount 0.13168860
## Recommendations 0.01467922
## OpenRevolvingMonthlyPayment
## BorrowerRate -0.011158846
## EstimatedLoss -0.029889251
## EstimatedReturn 0.016209249
## ProsperRating..numeric. 0.018444291
## ProsperScore 0.004127153
## EmploymentStatusDuration 0.130917140
## CreditScoreRangeLower 0.015489683
## CreditScoreRangeUpper 0.015489683
## OpenCreditLines 0.549298914
## OpenRevolvingAccounts 0.560629653
## OpenRevolvingMonthlyPayment 1.000000000
## CurrentDelinquencies -0.120141931
## AmountDelinquent -0.048446594
## StatedMonthlyIncome 0.351034261
## TotalProsperPaymentsBilled 0.011718293
## OnTimeProsperPayments 0.018903209
## LoanOriginalAmount 0.158422777
## Recommendations -0.027648115
## CurrentDelinquencies AmountDelinquent
## BorrowerRate 0.19247208 0.05832432
## EstimatedLoss 0.20761201 0.05694464
## EstimatedReturn 0.09167796 0.03521469
## ProsperRating..numeric. -0.19153561 -0.05811215
## ProsperScore -0.18054559 -0.06922222
## EmploymentStatusDuration 0.04086957 0.01865088
## CreditScoreRangeLower -0.20293154 -0.05950110
## CreditScoreRangeUpper -0.20293154 -0.05950110
## OpenCreditLines -0.13374945 -0.07166053
## OpenRevolvingAccounts -0.13089589 -0.06508789
## OpenRevolvingMonthlyPayment -0.12014193 -0.04844659
## CurrentDelinquencies 1.00000000 0.42922662
## AmountDelinquent 0.42922662 1.00000000
## StatedMonthlyIncome -0.02356340 0.02384595
## TotalProsperPaymentsBilled 0.05202131 0.02591850
## OnTimeProsperPayments 0.04572720 0.02174984
## LoanOriginalAmount -0.12062921 -0.02479796
## Recommendations 0.01086195 0.01361218
## StatedMonthlyIncome TotalProsperPaymentsBilled
## BorrowerRate -0.175402514 0.015204584
## EstimatedLoss -0.163109308 0.037546657
## EstimatedReturn -0.129163303 -0.026208202
## ProsperRating..numeric. 0.182653529 -0.028280944
## ProsperScore 0.149059321 0.048789718
## EmploymentStatusDuration 0.088275588 0.052261809
## CreditScoreRangeLower 0.141365364 -0.065953838
## CreditScoreRangeUpper 0.141365364 -0.065953838
## OpenCreditLines 0.245719475 0.054588933
## OpenRevolvingAccounts 0.167561010 0.017349217
## OpenRevolvingMonthlyPayment 0.351034261 0.011718293
## CurrentDelinquencies -0.023563403 0.052021306
## AmountDelinquent 0.023845950 0.025918499
## StatedMonthlyIncome 1.000000000 -0.003911465
## TotalProsperPaymentsBilled -0.003911465 1.000000000
## OnTimeProsperPayments -0.004680939 0.989833952
## LoanOriginalAmount 0.319067492 0.008807879
## Recommendations -0.008996549 0.108737733
## OnTimeProsperPayments LoanOriginalAmount
## BorrowerRate 0.002647098 -0.332415965
## EstimatedLoss 0.027420598 -0.373418167
## EstimatedReturn -0.037520478 -0.140509818
## ProsperRating..numeric. -0.016693974 0.378456072
## ProsperScore 0.060967879 0.265941623
## EmploymentStatusDuration 0.053694338 0.021701617
## CreditScoreRangeLower -0.053406989 0.296493221
## CreditScoreRangeUpper -0.053406989 0.296493221
## OpenCreditLines 0.062047323 0.138424850
## OpenRevolvingAccounts 0.025919597 0.131688600
## OpenRevolvingMonthlyPayment 0.018903209 0.158422777
## CurrentDelinquencies 0.045727195 -0.120629209
## AmountDelinquent 0.021749841 -0.024797957
## StatedMonthlyIncome -0.004680939 0.319067492
## TotalProsperPaymentsBilled 0.989833952 0.008807879
## OnTimeProsperPayments 1.000000000 0.013513340
## LoanOriginalAmount 0.013513340 1.000000000
## Recommendations 0.109053878 -0.016158229
## Recommendations
## BorrowerRate -0.007944508
## EstimatedLoss 0.015460914
## EstimatedReturn -0.033922843
## ProsperRating..numeric. -0.001580269
## ProsperScore 0.028654751
## EmploymentStatusDuration -0.010011644
## CreditScoreRangeLower 0.012406915
## CreditScoreRangeUpper 0.012406915
## OpenCreditLines 0.003112712
## OpenRevolvingAccounts 0.014679223
## OpenRevolvingMonthlyPayment -0.027648115
## CurrentDelinquencies 0.010861954
## AmountDelinquent 0.013612180
## StatedMonthlyIncome -0.008996549
## TotalProsperPaymentsBilled 0.108737733
## OnTimeProsperPayments 0.109053878
## LoanOriginalAmount -0.016158229
## Recommendations 1.000000000There is a high correlation between BorrowerRate and Estimated Loss.
This makes sense since investors should require higher rates for riskier assets.
As expected, the BorrowerRate is negatively correlated with ProsperScore,
ProsperRating and CreditRating. I wonder what factors are used to determine
the interest rate for each listing.
From the scatterplot, it is not very clear whether StatedMonthlyIncome is
correlated with the BorroweRate. Let’s try a boxplot
## item group1 vars n mean sd median trimmed
## X17 7 Not displayed 1 7741 0.1891813 0.06917048 0.18800 0.1896773
## X18 8 Not employed 1 806 0.2467031 0.07621766 0.25995 0.2543969
## X11 1 $0 1 621 0.1951807 0.08035309 0.17500 0.1909187
## X12 2 $1-24,999 1 7274 0.2205589 0.07756052 0.21990 0.2224778
## X14 4 $25,000-49,999 1 32192 0.2071791 0.07445022 0.20150 0.2065862
## X15 5 $50,000-74,999 1 31050 0.1903349 0.07315170 0.18000 0.1872053
## X16 6 $75,000-99,999 1 16916 0.1809260 0.07276417 0.16990 0.1768378
## X13 3 $100,000+ 1 17337 0.1692426 0.07095827 0.15500 0.1635624
## mad min max range skew kurtosis se
## X17 0.07857780 0.000 0.4975 0.4975 0.0546311 -0.8452869 0.0007861805
## X18 0.08562015 0.040 0.3500 0.3100 -0.6718467 -0.7072609 0.0026846524
## X11 0.07413000 0.005 0.3500 0.3450 0.4473474 -0.7212559 0.0032244586
## X12 0.10140984 0.000 0.3600 0.3600 -0.1090307 -1.0869585 0.0009093981
## X14 0.08702862 0.000 0.3600 0.3600 0.1032195 -0.9965130 0.0004149464
## X15 0.08154300 0.000 0.3600 0.3600 0.3354095 -0.8266400 0.0004151391
## X16 0.07561260 0.000 0.3600 0.3600 0.4370948 -0.7218477 0.0005594596
## X13 0.06834786 0.000 0.3600 0.3600 0.6252797 -0.4377076 0.0005389097From the boxplot, it is as expected that the higher the income the borrower
has, the less risky the loan is. However, it is strange that the median
BorrowerRate for the group whose IncomeRange = $0 is lower than those with
IncomeRange $1-74,999.
## item group1 vars n mean sd median trimmed
## X11 1 1 2255 0.1855432 0.07145271 0.1780 0.1839139
## X12 2 Employed 1 67322 0.1927906 0.07185477 0.1840 0.1907290
## X13 3 Full-time 1 26355 0.1870060 0.08154179 0.1724 0.1816353
## X14 4 Not available 1 5347 0.1914925 0.06801645 0.1900 0.1930621
## X15 5 Not employed 1 835 0.2440788 0.07710970 0.2599 0.2514864
## X16 6 Other 1 3806 0.2136962 0.07153107 0.2099 0.2152006
## X17 7 Part-time 1 1088 0.1844003 0.08013861 0.1690 0.1782154
## X18 8 Retired 1 795 0.1944420 0.08514592 0.1829 0.1906915
## X19 9 Self-employed 1 6134 0.2022686 0.07669170 0.1899 0.2010586
## mad min max range skew kurtosis se
## X11 0.08821470 0.0000 0.4975 0.4975 0.29486124 -0.2447385 0.0015046845
## X12 0.08287734 0.0450 0.3600 0.3150 0.23778322 -0.9415730 0.0002769345
## X13 0.09177294 0.0000 0.3600 0.3600 0.46765034 -0.8037381 0.0005022833
## X14 0.08154300 0.0000 0.3000 0.3000 -0.06684691 -1.1089883 0.0009301625
## X15 0.08569428 0.0100 0.3500 0.3400 -0.63222656 -0.7430716 0.0026684911
## X16 0.08821470 0.0565 0.3500 0.2935 -0.05373143 -1.0644397 0.0011594722
## X17 0.08665797 0.0100 0.3500 0.3400 0.56987018 -0.6229219 0.0024295583
## X18 0.10808154 0.0500 0.3500 0.3000 0.32555855 -1.0725602 0.0030198145
## X19 0.08910426 0.0100 0.3500 0.3400 0.21130832 -1.1135670 0.0009792114As expected, the median BorrowerRate for borrowers who are employed are
lower than those who are not employed. However, I am curious why the
median BorrowerRate for borrowers who are part-timers are lower than
those who are full-timers.
From the scatterplot, it is clear that the higher the credit score the
borrower has, the lower the BorrowerRate is required.
## item group1 vars n mean sd median trimmed
## X11 1 1 29084 0.18325969 0.07455439 0.1700 0.17891657
## X13 3 AA 1 5372 0.07912197 0.01477932 0.0779 0.07754751
## X12 2 A 1 14551 0.11294028 0.01728524 0.1119 0.11214183
## X14 4 B 1 15581 0.15445193 0.01987918 0.1509 0.15296343
## X15 5 C 1 18345 0.19443037 0.02420360 0.1914 0.19281748
## X16 6 D 1 14274 0.24641703 0.02537442 0.2492 0.24675070
## X17 7 E 1 9795 0.29333845 0.02603741 0.2925 0.29349879
## X18 8 HR 1 6935 0.31732500 0.01889059 0.3177 0.31730036
## mad min max range skew kurtosis se
## X11 0.07709520 0.0000 0.4975 0.4975 0.4981315 -0.5109810 0.0004371658
## X13 0.01037820 0.0400 0.2100 0.1700 1.3537538 4.4750141 0.0002016445
## X12 0.01779120 0.0498 0.2150 0.1652 0.4983194 0.9555580 0.0001432943
## X14 0.01630860 0.0693 0.3500 0.2807 0.8327298 1.6341363 0.0001592578
## X15 0.02283204 0.0895 0.3500 0.2605 0.6175170 1.0506435 0.0001786986
## X16 0.02698332 0.1157 0.3500 0.2343 -0.1766243 0.9695377 0.0002123847
## X17 0.03157938 0.1479 0.3600 0.2121 -0.2818067 0.6902452 0.0002630847
## X18 0.00000000 0.1779 0.3600 0.1821 -3.3437225 22.4649500 0.0002268414This is as expected since I suspect that the credit score of the borrower
will determine the credit rating of the loan, and the worse loan rating will
require the higher interest rate.
## item group1 vars n mean sd median trimmed
## X11 1 12 1 1614 0.1500807 0.06785817 0.1434 0.1478215
## X12 2 36 1 87778 0.1934855 0.07925234 0.1815 0.1910764
## X13 3 60 1 24545 0.1929907 0.05566590 0.1870 0.1904665
## mad min max range skew kurtosis se
## X11 0.08376690 0.0400 0.2669 0.2269 0.2374626 -1.1537038 0.0016890807
## X12 0.09696204 0.0000 0.4975 0.4975 0.2586755 -1.0563516 0.0002674972
## X13 0.06004530 0.0669 0.3304 0.2635 0.3729595 -0.5508182 0.0003553102As expected, the shorter term (12-month) loan has lower median BorrowerRate than the longer term (36 and 60 month) has, since the longer-term loans are more exposed to interest rate risks. However, I am surprised that the difference between the BorrowerRate of 36-month loan and 60-month loan is small.
## item group1 vars n mean sd median trimmed
## X11 1 0 1 16965 0.1815787 0.06623780 0.17590 0.1802810
## X12 2 1 1 58308 0.1882613 0.07166938 0.17740 0.1846506
## X13 3 2 1 7433 0.1981773 0.07912518 0.19050 0.1973414
## X14 4 3 1 7189 0.2005974 0.08069144 0.19000 0.1996387
## X15 5 4 1 2395 0.1806073 0.08726652 0.15750 0.1737612
## X16 6 5 1 756 0.2052496 0.09299629 0.19000 0.2031168
## X17 7 6 1 2572 0.2068046 0.08307643 0.20490 0.2074184
## X18 8 7 1 10494 0.2134143 0.08410435 0.21510 0.2152115
## X19 9 8 1 199 0.1857457 0.07343137 0.17680 0.1827484
## X110 10 9 1 85 0.1739812 0.06510994 0.16390 0.1718942
## X111 11 10 1 91 0.2259484 0.07248261 0.24490 0.2305000
## X112 12 11 1 217 0.1960535 0.07106779 0.19790 0.1955937
## X113 13 12 1 59 0.2002458 0.08765140 0.19700 0.2016980
## X114 14 13 1 1996 0.2237560 0.07267137 0.22870 0.2277389
## X115 15 14 1 876 0.1909197 0.07220494 0.18425 0.1894068
## X116 16 15 1 1522 0.2090848 0.07323964 0.20990 0.2104245
## X117 17 16 1 304 0.1975829 0.07541715 0.19080 0.1968902
## X118 18 17 1 52 0.2073250 0.07876661 0.21360 0.2098571
## X119 19 18 1 885 0.2065225 0.07221377 0.20850 0.2076891
## X120 20 19 1 768 0.2075052 0.07545702 0.20850 0.2090977
## X121 21 20 1 771 0.2065501 0.06945222 0.20850 0.2071968
## mad min max range skew kurtosis se
## X11 0.07946736 0.0000 0.4975 0.4975 0.196884092 -0.7896442 0.0005085445
## X12 0.07694694 0.0000 0.3600 0.3600 0.400053492 -0.7491043 0.0002968039
## X13 0.09711030 0.0400 0.3500 0.3100 0.123992168 -1.1096628 0.0009177674
## X14 0.09799986 0.0000 0.3600 0.3600 0.158337634 -1.1379884 0.0009516850
## X15 0.07857780 0.0100 0.3600 0.3500 0.620523367 -0.6886193 0.0017831789
## X16 0.11275173 0.0100 0.3600 0.3500 0.265264207 -1.2460401 0.0033822423
## X17 0.10267005 0.0499 0.3500 0.3001 -0.006074911 -1.2288338 0.0016381073
## X18 0.10615416 0.0000 0.3600 0.3600 -0.115549955 -1.1855496 0.0008210090
## X19 0.08584254 0.0605 0.3304 0.2699 0.341296047 -1.0297029 0.0052054117
## X110 0.07413000 0.0628 0.3177 0.2549 0.278219377 -0.7904024 0.0070621642
## X111 0.09592422 0.0565 0.3304 0.2739 -0.347779888 -0.9495735 0.0075982421
## X112 0.07709520 0.0565 0.3304 0.2739 0.050226753 -0.8871763 0.0048243961
## X113 0.10689546 0.0565 0.3304 0.2739 0.009718636 -1.3009625 0.0114112409
## X114 0.09325554 0.0565 0.3304 0.2739 -0.272367419 -1.0195918 0.0016266087
## X115 0.08799231 0.0565 0.3304 0.2739 0.171303401 -1.0336329 0.0024395788
## X116 0.08984556 0.0565 0.3304 0.2739 -0.066058939 -1.0967414 0.0018773226
## X117 0.08613906 0.0608 0.3304 0.2696 0.112746351 -1.0711814 0.0043254700
## X118 0.09792573 0.0605 0.3304 0.2699 -0.179007911 -1.2021691 0.0109229631
## X119 0.07917084 0.0565 0.3304 0.2739 -0.052175433 -0.9298750 0.0024274393
## X120 0.09295902 0.0565 0.3304 0.2739 -0.047693327 -1.1002904 0.0027228208
## X121 0.08213604 0.0605 0.3304 0.2699 -0.049127896 -0.9542616 0.0025012608Differnt categories of loans can have different BorrowerRate,
but I will not go deeper.
There is no clear correlation between number of credit lines/open revolving
accounts and BorrowerRate. However, the number of delinquencies/public records
is positively correlated with the BorrowerRate.
The size of the loan does not seem to be correlated with the BorrowerRate.
##
## Call:
## lm(formula = BorrowerRate ~ StatedMonthlyIncome, data = loan_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.19740 -0.05870 -0.00920 0.05823 1.67782
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.978e-01 2.760e-04 716.62 <2e-16 ***
## StatedMonthlyIncome -8.902e-07 2.952e-08 -30.16 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07452 on 113935 degrees of freedom
## Multiple R-squared: 0.007918, Adjusted R-squared: 0.007909
## F-statistic: 909.3 on 1 and 113935 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = BorrowerRate ~ CreditScoreRangeLower, data = loan_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.49873 -0.05051 -0.01165 0.04585 0.21868
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.487e-01 2.041e-03 268.9 <2e-16 ***
## CreditScoreRangeLower -5.190e-04 2.963e-06 -175.2 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0663 on 113344 degrees of freedom
## (591 observations deleted due to missingness)
## Multiple R-squared: 0.213, Adjusted R-squared: 0.213
## F-statistic: 3.068e+04 on 1 and 113344 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = BorrowerRate ~ EmploymentStatus, data = loan_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.234079 -0.058791 -0.008791 0.057809 0.311957
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.185543 0.001569 118.270 < 2e-16 ***
## EmploymentStatusEmployed 0.007247 0.001595 4.544 5.52e-06 ***
## EmploymentStatusFull-time 0.001463 0.001635 0.895 0.37082
## EmploymentStatusNot available 0.005949 0.001871 3.180 0.00147 **
## EmploymentStatusNot employed 0.058536 0.003018 19.396 < 2e-16 ***
## EmploymentStatusOther 0.028153 0.001980 14.221 < 2e-16 ***
## EmploymentStatusPart-time -0.001143 0.002750 -0.416 0.67770
## EmploymentStatusRetired 0.008899 0.003073 2.896 0.00378 **
## EmploymentStatusSelf-employed 0.016725 0.001835 9.116 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0745 on 113928 degrees of freedom
## Multiple R-squared: 0.008622, Adjusted R-squared: 0.008552
## F-statistic: 123.9 on 8 and 113928 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = BorrowerRate ~ CurrentDelinquencies, data = loan_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.47348 -0.05815 -0.00904 0.05806 0.17106
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1889351 0.0002282 827.96 <2e-16 ***
## CurrentDelinquencies 0.0066680 0.0001105 60.35 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07357 on 113238 degrees of freedom
## (697 observations deleted due to missingness)
## Multiple R-squared: 0.03116, Adjusted R-squared: 0.03115
## F-statistic: 3642 on 1 and 113238 DF, p-value: < 2.2e-16The R-Squared values show that it is not sufficient to explain the borrower
rate with just one variable.
Bivariate Analysis
Talk about some of the relationships you observed in this part of the
investigation. How did the feature(s) of interest vary with other features in
the dataset?
The BorrowerRate is positively and strongly correlated with the Prosper Credit Rating. It is likely that the rate ranges are determined by the loan credit rating.
Other features that are positively, though less strongly correlated with BorrowerRate, include staetd monthly income, borrower’s credit score, number of delinquents, and number of public records in the past 10 years.
The features that do not seem to be correlated with BorrowerRate are number of open credit lines, number of open revolving accounts, and size of the loan.
Did you observe any interesting relationships between the other features
(not the main feature(s) of interest)? The interest rate also depends on the
term of the loan. Shorter term loans have lower required interest rates than longer term loans do. Another interesting observation is that the median interest rate of the loan category 10 (Cosmetic Operation) is significantly higher than that of loan category 4 (Personal Loan), 25% vs 15%.
What was the strongest relationship you found?
The BorrowerRate and the Prosper Credit Rating of the listing are positively and strongly correlated. This is expected as the rating is classified by its riskiness. The more interesting question is how do we determine the riskiness of each listing.
Multivariate Plots Section
The Prosper settlement records do not seem to be correlated to the BorrowerRate.
Short term listings (12-month) clearly have lower BorrowRate. Also, as the
credit score increases, the BorrowRate for short-term listings seem to decrease.
However, for the longer term listings (36-month and 60-month), the variance is
high. It is not clear which one has lower BorrowRate, and whether credit
score matters.
This plot is crowded by Employed status. It does not tell anything much.
BorrowerRate are clearly layered by ProsperRating..Alpha. The BorrowerRate
also has moderate negative correlation with the credit score of the borrower.
Shorter term listings clearly have lower BorrowerRate. The rates are further
layered by ProsperRating..Alpha. We can also see moderate negative correlation
between BorrowerRate and CreditScore.
##
## Calls:
## m1: lm(formula = I(BorrowerRate) ~ CreditScoreRangeLower, data = subset(loan_data,
## , as.character(ProsperScore) != ""))
## m2: lm(formula = I(BorrowerRate) ~ CreditScoreRangeLower + StatedMonthlyIncome,
## data = subset(loan_data, , as.character(ProsperScore) !=
## ""))
## m3: lm(formula = I(BorrowerRate) ~ CreditScoreRangeLower + StatedMonthlyIncome +
## CurrentDelinquencies, data = subset(loan_data, , as.character(ProsperScore) !=
## ""))
## m4: lm(formula = I(BorrowerRate) ~ CreditScoreRangeLower + StatedMonthlyIncome +
## CurrentDelinquencies + ProsperRating..Alpha., data = subset(loan_data,
## , as.character(ProsperScore) != ""))
## m5: lm(formula = I(BorrowerRate) ~ CreditScoreRangeLower + StatedMonthlyIncome +
## CurrentDelinquencies + ProsperRating..Alpha. + Term, data = subset(loan_data,
## , as.character(ProsperScore) != ""))
## m6: lm(formula = I(BorrowerRate) ~ CreditScoreRangeLower + StatedMonthlyIncome +
## CurrentDelinquencies + ProsperRating..Alpha. + Term + EmploymentStatus,
## data = subset(loan_data, , as.character(ProsperScore) !=
## ""))
##
## ===================================================================================================================================
## m1 m2 m3 m4 m5 m6
## -----------------------------------------------------------------------------------------------------------------------------------
## (Intercept) 0.549*** 0.548*** 0.567*** 0.381*** 0.362*** 0.358***
## (0.002) (0.002) (0.002) (0.001) (0.001) (0.002)
## CreditScoreRangeLower -0.001*** -0.001*** -0.001*** -0.000*** -0.000*** -0.000***
## (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
## StatedMonthlyIncome -0.000*** -0.000*** -0.000*** -0.000*** -0.000**
## (0.000) (0.000) (0.000) (0.000) (0.000)
## CurrentDelinquencies 0.000 0.003*** 0.003*** 0.003***
## (0.000) (0.000) (0.000) (0.000)
## ProsperRating..Alpha.: A -0.041*** -0.044*** -0.042***
## (0.000) (0.000) (0.001)
## ProsperRating..Alpha.: AA -0.061*** -0.062*** -0.059***
## (0.001) (0.001) (0.001)
## ProsperRating..Alpha.: B -0.007*** -0.012*** -0.010***
## (0.000) (0.000) (0.001)
## ProsperRating..Alpha.: C 0.028*** 0.021*** 0.023***
## (0.000) (0.000) (0.001)
## ProsperRating..Alpha.: D 0.076*** 0.072*** 0.074***
## (0.000) (0.000) (0.001)
## ProsperRating..Alpha.: E 0.117*** 0.115*** 0.116***
## (0.000) (0.000) (0.001)
## ProsperRating..Alpha.: HR 0.146*** 0.146*** 0.147***
## (0.001) (0.000) (0.001)
## Term 0.001*** 0.001***
## (0.000) (0.000)
## EmploymentStatus: Employed 0.007***
## (0.001)
## EmploymentStatus: Full-time 0.012***
## (0.001)
## EmploymentStatus: Not available -0.000
## (0.001)
## EmploymentStatus: Not employed 0.022***
## (0.002)
## EmploymentStatus: Other 0.007***
## (0.001)
## EmploymentStatus: Part-time 0.006***
## (0.001)
## EmploymentStatus: Retired 0.013***
## (0.002)
## EmploymentStatus: Self-employed 0.012***
## (0.001)
## -----------------------------------------------------------------------------------------------------------------------------------
## R-squared 0.213 0.215 0.225 0.755 0.762 0.764
## adj. R-squared 0.213 0.215 0.225 0.755 0.762 0.764
## sigma 0.066 0.066 0.066 0.037 0.036 0.036
## F 30684.346 15478.726 10984.621 34875.500 32937.420 19264.465
## p 0.000 0.000 0.000 0.000 0.000 0.000
## Log-likelihood 146748.641 146856.110 147496.000 212647.834 214285.781 214729.232
## Deviance 498.165 497.221 489.984 155.040 150.620 149.444
## AIC -293491.283 -293704.220 -294982.000 -425271.667 -428545.563 -429416.464
## BIC -293462.368 -293665.667 -294933.814 -425156.020 -428420.278 -429214.081
## N 113346 113346 113240 113240 113240 113240
## ===================================================================================================================================The variables in this linear model can account for 76.4% of the variance in the BorrowerRate of the loan listings. The most important factor is the credit rating of the loan, followed by credit score of the borrower. Other variables poorly contribute to the prediction.
Multivariate Analysis
Talk about some of the relationships you observed in this part of the
investigation. Were there features that strengthened each other in terms of
looking at your feature(s) of interest?
ProsperRating..Alpha clearly determine the ranges of the BorrowRate. The BorrowerRate also has moderate negative correlation with the credit score of the borrower. The stated monthly income has weaker correlation to the borrower rate.
The 12-month clearly has lower borrower rate than those of 36-month and 60-month loans.
Were there any interesting or surprising interactions between features?
Yes, I am surprised that the monthly stated income does not matter much in determining the borrower rates. Also, I would have thought that the 60-month loans would have higher interest rates than those of the 36-month loans since the former are more exposed to the interest rate change risk.
OPTIONAL: Did you create any models with your dataset? Discuss the
strengths and limitations of your model.
Yes, I created models to predict a loan’s borrower rate from borrower’s credit score, stated monthly income, Prosper credit rating of the listing, loan term, and borrower’s employment status. These variables can account for 76.4% of the variance in the BorrowerRate. However, I still cannot find determining factors for credit rating of the loan listings.
Final Plots and Summary
Plot One
Description One
The loans are categorized by its riskiness from low risk to high risk (AA -> HR). The riskier the loans are, the higher interest rates they require.
Plot Two
Description Two
Shorter term listings have lower BorrowerRate. The rates are further layered
by ProsperRating..Alpha. We can also see moderate negative correlation between
BorrowerRate and CreditScore.
Plot Three
Description Three
In comparison with Plot2, the positive correlation between borrower rate and
stated monthly income is weaker than that between borrower rate and borrower’s
credit score although one would expect that borrower’s income should play a
critical role in determining the riskness of a loan.
Reflection
The loan data set has 113,937 records across 81 variables. There is a clear gap during pre-2009 and post-2009, possibly due to the Financial Crisis in late 2008. The focus of this EDA is on the loan listed post-2009.
My objective is to determine what determines the interest rate a borrower has to pay for his loan. I suspect that the factors would include things like his credit score, his past settlement history, amount of debts he hold, his income, his employment, and loan term.
Firstly, I examined many variables, plotting out their histograms and see their staitstics. Next I explored the relationships between two variables. As expected, there is a strong positive correlation between interest rate and the credit rating of a loan, but my main question is what determines which credit rating a loan will receive. The scatterplot between interest rate and borrower’s credit score shows a moderate positive correlation. However, other variables such as number of open credit lines, number of open revolving accounts, stated monthly income, and loan size do not show clear correlation.
When I used facet_wrap to see whether the loan term affects interest rates, I found that the 12-month loan has lower interest rate as expected. However, I am surprised to find that there is almost no difference in the median interest rates of 36-month and 60-month loans.
When I tried to find a linear model to predict a borrower’s interest rate, only credit rating of the loan and borrower’s credit score significantly contribute to the R-squared value. Other factors’ contributions are negligible.
For future exploration, I may have to include things like loan categories, occupations, and collaterals in order to better determine the required interest rates.